So far, the kernel method achieves the best localization performance with accuracy up to 2 m when \(k\ge 3\) .
Next, we compared the result achieved by our proposed selection strategy to the above kNN that relies merely on the top-\(k\)  neighbors for performance improvement.
Note that kNN is the common approach used by many fingerprint-based location estimations, for example, [1]} used kNN to estimate the location based on the fingerprint constructed from BLE beacons, whereas [2]} used kNN with WiFi-based fingerprint for location estimation.
However, conventional kNN matches the fingerprint directly by comparing the observation vector and the fingerprint database without considering any selection strategy.
To verify that our selection strategy can further improve the performance of kNN, we evaluated the effect of the number of neighbors on the localization performance for the fingerprint refined by our selection strategy and the original fingerprint.
